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Bias correction of daily precipitation over South Korea from the Long-Term Reanalysis using a Composite Gamma-Pareto Distribution Approach

Research output: Contribution to journalArticle

Original languageEnglish
Article numbernh2019127
JournalHydrology Research
DateAccepted/In press - 15 Feb 2019
DatePublished (current) - 4 Apr 2019


The long-term precipitation data plays an important role in climate impact studies, but the observation for a given catchment is very limited. To significantly expand our sample size for the extreme rainfall analysis, we considered ERA-20c, a century-long reanalysis daily precipitation provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Preliminary studies have already indicated that ERA-20c can reproduce the mean reasonably well, but rainfall intensity is underestimated while wet-day frequency is overestimated. Thus, we first adopted a relatively simple approach to adjust the frequency of wet-days by imposing an optimal threshold. Moreover, we introduced a quantile mapping approach based on a composite distribution of a generalized Pareto distribution for the upper tail (e.g. 95th and 99th percentile), and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases over the conventional method for the extremes. We suggested an interpolation method for the set of parameters of bias correction approach in ungauged catchments. A comparison of the corrected precipitation using spatially interpolated parameters shows that the proposed modelling scheme, particularly with the 99th percentile, can reliably reduce the systematic bias. The findings in this study suggest that the proposed approach can provide a useful alternative to the bias correction of a regional scale modelled data with a limited network of rain gauges.

    Research areas

  • statistical bias correction, reanalysis, quantile mapping, parameter contour map, ERA-20c, composite distribution



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